{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T03:29:20Z","timestamp":1785036560765,"version":"3.55.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276164"],"award-info":[{"award-number":["62276164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602296"],"award-info":[{"award-number":["61602296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["22ZR1427000"],"award-info":[{"award-number":["22ZR1427000"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Oriental Talent Program-Youth Program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00530-025-01821-6","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T14:11:06Z","timestamp":1746454266000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DRMFE: optimizing incomplete multi-view clustering through dual recovery and multi-scale feature enhancement"],"prefix":"10.1007","volume":"31","author":[{"given":"Liju","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,5]]},"reference":[{"issue":"12","key":"1821_CR1","doi-asserted-by":"publisher","first-page":"12350","DOI":"10.1109\/TKDE.2023.3270311","volume":"35","author":"U Fang","year":"2023","unstructured":"Fang, U., Li, M., Li, J., Gao, L., Jia, T., Zhang, Y.: A comprehensive survey on multi-view clustering. IEEE Trans. Knowl. Data Eng. 35(12), 12350\u201312368 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1821_CR2","unstructured":"Nie, F., Li, J., Li, X., et al.: Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: IJCAI, vol. 9, pp. 1881\u20131887 (2016)"},{"key":"1821_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, C., Hu, Q., Fu, H., Zhu, P., Cao, X.: Latent multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4279\u20134287 (2017)","DOI":"10.1109\/CVPR.2017.461"},{"key":"1821_CR4","first-page":"2148","volume":"34","author":"E Pan","year":"2021","unstructured":"Pan, E., Kang, Z.: Multi-view contrastive graph clustering. Adv. Neural. Inf. Process. Syst. 34, 2148\u20132159 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1821_CR5","unstructured":"Peng, X., Huang, Z., Lv, J., Zhu, H., Zhou, J.T.: Comic: multi-view clustering without parameter selection. In: International Conference on Machine Learning, pp. 5092\u20135101. PMLR (2019)"},{"key":"1821_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111192","volume":"160","author":"B Cai","year":"2025","unstructured":"Cai, B., Lu, G.-F., Guo, X., Wu, T.: Tensorized latent representation with automatic dimensionality selection for multi-view clustering. Pattern Recognit. 160, 111192 (2025)","journal-title":"Pattern Recognit."},{"key":"1821_CR7","doi-asserted-by":"publisher","first-page":"6621","DOI":"10.1109\/TMM.2024.3355649","volume":"26","author":"B Cai","year":"2024","unstructured":"Cai, B., Lu, G.-F., Li, H., Song, W.: Tensorized scaled simplex representation for multi-view clustering. IEEE Trans. Multimed. 26, 6621\u20136631 (2024)","journal-title":"IEEE Trans. Multimed."},{"key":"1821_CR8","doi-asserted-by":"crossref","unstructured":"Zhuge, W., Hou, C., Liu, X., Tao, H., Yi, D.: Simultaneous representation learning and clustering for incomplete multi-view data. In: IJCAI, vol. 7, pp. 4482\u20134488 (2019)","DOI":"10.24963\/ijcai.2019\/623"},{"key":"1821_CR9","doi-asserted-by":"crossref","unstructured":"Guo, J., Ye, J.: Anchors bring ease: an embarrassingly simple approach to partial multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 118\u2013125 (2019)","DOI":"10.1609\/aaai.v33i01.3301118"},{"key":"1821_CR10","first-page":"1","volume":"20","author":"M Li","year":"2024","unstructured":"Li, M., Zhang, R., Zhang, Y., Piao, X., Zhao, S., Yin, B.: SCAE: structural contrastive auto-encoder for incomplete multi-view representation learning. ACM Trans. Multimed. Comput. Commun. Appl. 20, 1\u201324 (2024)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"1821_CR11","doi-asserted-by":"publisher","first-page":"124258","DOI":"10.1016\/j.eswa.2024.124258","volume":"252","author":"Z Shu","year":"2024","unstructured":"Shu, Z., Luo, Y., Huang, Y., Mao, C., Yu, Z.: View-interactive attention information alignment-guided fusion for incomplete multi-view clustering. Expert Syst. Appl. 252, 124258 (2024)","journal-title":"Expert Syst. Appl."},{"key":"1821_CR12","doi-asserted-by":"crossref","unstructured":"Jin, J., Wang, S., Dong, Z., Liu, X., Zhu, E.: Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11600\u201311609 (2023)","DOI":"10.1109\/CVPR52729.2023.01116"},{"issue":"4","key":"1821_CR13","first-page":"4447","volume":"45","author":"Y Lin","year":"2022","unstructured":"Lin, Y., Gou, Y., Liu, X., Bai, J., Lv, J., Peng, X.: Dual contrastive prediction for incomplete multi-view representation learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4447\u20134461 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1821_CR14","doi-asserted-by":"crossref","unstructured":"Wen, J., Zhang, Z., Xu, Y., Zhang, B., Fei, L., Liu, H.: Unified embedding alignment with missing views inferring for incomplete multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5393\u20135400 (2019)","DOI":"10.1609\/aaai.v33i01.33015393"},{"key":"1821_CR15","doi-asserted-by":"publisher","first-page":"10001","DOI":"10.1109\/TMM.2024.3405650","volume":"26","author":"H Wang","year":"2024","unstructured":"Wang, H., Yao, M., Chen, Y., Xu, Y., Liu, H., Jia, W., Fu, X., Wang, Y.: Manifold-based incomplete multi-view clustering via bi-consistency guidance. IEEE Trans. Multimed. 26, 10001\u201310014 (2024)","journal-title":"IEEE Trans. Multimed."},{"key":"1821_CR16","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1109\/TMM.2024.3521771","volume":"27","author":"M Yao","year":"2024","unstructured":"Yao, M., Wang, H., Chen, Y., Fu, X.: Between\/within view information completing for tensorial incomplete multi-view clustering. IEEE Trans. Multimed. 27, 1538\u20131550 (2024)","journal-title":"IEEE Trans. Multimed."},{"key":"1821_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, X., Zhou, S., Liu, X., Wang, Z., Liang, K., Tu, W., Li, L., Duan, J., Chen, C.: Hard sample aware network for contrastive deep graph clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 8914\u20138922 (2023)","DOI":"10.1609\/aaai.v37i7.26071"},{"key":"1821_CR18","doi-asserted-by":"crossref","unstructured":"Tu, W., Zhou, S., Liu, X., Guo, X., Cai, Z., Zhu, E., Cheng, J.: Deep fusion clustering network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9978\u20139987 (2021)","DOI":"10.1609\/aaai.v35i11.17198"},{"key":"1821_CR19","doi-asserted-by":"crossref","unstructured":"Xu, J., Li, C., Ren, Y., Peng, L., Mo, Y., Shi, X., Zhu, X.: Deep incomplete multi-view clustering via mining cluster complementarity. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8761\u20138769 (2022)","DOI":"10.1609\/aaai.v36i8.20856"},{"key":"1821_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Q., Ding, Z., Tao, Z., Gao, Q., Fu, Y.: Partial multi-view clustering via consistent gan. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1290\u20131295. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00174"},{"key":"1821_CR21","doi-asserted-by":"crossref","unstructured":"Xu, C., Guan, Z., Zhao, W., Wu, H., Niu, Y., Ling, B.: Adversarial incomplete multi-view clustering. In: IJCAI, vol. 7, 3933\u20133939 (2019)","DOI":"10.24963\/ijcai.2019\/546"},{"issue":"6","key":"1821_CR22","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.1109\/TSMC.2024.3374068","volume":"54","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Chen, X., Hu, Q., Xiao, Y., Wang, B., Sheng, B., Chen, C.P.: Clustering environment aware learning for active domain adaptation. IEEE Trans. Syst. Man Cybern. Syst. 54(6), 3891\u20133904 (2024)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"1821_CR23","doi-asserted-by":"publisher","first-page":"6821","DOI":"10.1109\/TMM.2022.3214776","volume":"25","author":"J Zhu","year":"2022","unstructured":"Zhu, J., Zhang, Q., Fei, L., Cai, R., Xie, Y., Sheng, B., Yang, X.: FFFN: frame-by-frame feedback fusion network for video super-resolution. IEEE Trans. Multimed. 25, 6821\u20136835 (2022)","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"1821_CR24","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TNNLS.2022.3175775","volume":"35","author":"A Karambakhsh","year":"2022","unstructured":"Karambakhsh, A., Sheng, B., Li, P., Li, H., Kim, J., Jung, Y., Chen, C.P.: Sparsevoxnet: 3-d object recognition with sparsely aggregation of 3-d dense blocks. IEEE Trans. Neural Netw. Learn. Syst. 35(1), 532\u2013546 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1821_CR25","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.knosys.2019.05.021","volume":"180","author":"C Li","year":"2019","unstructured":"Li, C., Wang, S., Yang, D., Philip, S.Y., Liang, Y., Li, Z.: Adversarial learning for multi-view network embedding on incomplete graphs. Knowl.-Based Syst. 180, 91\u2013103 (2019)","journal-title":"Knowl.-Based Syst."},{"issue":"10","key":"1821_CR26","doi-asserted-by":"publisher","first-page":"10490","DOI":"10.1109\/TCYB.2021.3062830","volume":"52","author":"C Xu","year":"2021","unstructured":"Xu, C., Liu, H., Guan, Z., Wu, X., Tan, J., Ling, B.: Adversarial incomplete multiview subspace clustering networks. IEEE Trans. Cybern. 52(10), 10490\u201310503 (2021)","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"1821_CR27","first-page":"100004","volume":"1","author":"A Aggarwal","year":"2021","unstructured":"Aggarwal, A., Mittal, M., Battineni, G.: Generative adversarial network: an overview of theory and applications. Int. J. Inf. Manag. Data Insights 1(1), 100004 (2021)","journal-title":"Int. J. Inf. Manag. Data Insights"},{"issue":"4","key":"1821_CR28","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.1109\/TKDE.2021.3130191","volume":"35","author":"J Gui","year":"2021","unstructured":"Gui, J., Sun, Z., Wen, Y., Tao, D., Ye, J.: A review on generative adversarial networks: algorithms, theory, and applications. IEEE Trans. Knowl. Data Eng. 35(4), 3313\u20133332 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1821_CR29","doi-asserted-by":"publisher","first-page":"6551","DOI":"10.1109\/TMM.2022.3210376","volume":"25","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Chang, D., Fu, Z., Wen, J., Zhao, Y.: Graph contrastive partial multi-view clustering. IEEE Trans. Multimed. 25, 6551\u20136562 (2022)","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"1821_CR30","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/TCSVT.2022.3201822","volume":"33","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Chang, D., Fu, Z., Wen, J., Zhao, Y.: Incomplete multiview clustering via cross-view relation transfer. IEEE Trans. Circuits Syst. Video Technol. 33(1), 367\u2013378 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1821_CR31","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.patcog.2018.11.007","volume":"88","author":"S Huang","year":"2019","unstructured":"Huang, S., Kang, Z., Tsang, I.W., Xu, Z.: Auto-weighted multi-view clustering via kernelized graph learning. Pattern Recognit. 88, 174\u2013184 (2019)","journal-title":"Pattern Recognit."},{"key":"1821_CR32","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhou, S., Wang, Y., Li, M., Dou, Y., Zhu, E., Yin, J.: Optimal neighborhood kernel clustering with multiple kernels. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10895"},{"issue":"5","key":"1821_CR33","first-page":"1191","volume":"42","author":"X Liu","year":"2019","unstructured":"Liu, X., Zhu, X., Li, M., Wang, L., Zhu, E., Liu, T., Kloft, M., Shen, D., Yin, J., Gao, W.: Multiple kernel $$k$$ k-means with incomplete kernels. IEEE Trans. Pattern Anal. Mach. Intell. 42(5), 1191\u20131204 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1821_CR34","doi-asserted-by":"publisher","unstructured":"Menglei, H., Chen, S.: Doubly aligned incomplete multi-view clustering, pp. 2262\u20132268 (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/313","DOI":"10.24963\/ijcai.2018\/313"},{"key":"1821_CR35","doi-asserted-by":"crossref","unstructured":"Li, S.-Y., Jiang, Y., Zhou, Z.-H.: Partial multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8973"},{"issue":"20","key":"1821_CR36","doi-asserted-by":"publisher","first-page":"5755","DOI":"10.3390\/s20205755","volume":"20","author":"P Zhang","year":"2020","unstructured":"Zhang, P., Wang, S., Hu, J., Cheng, Z., Guo, X., Zhu, E., Cai, Z.: Adaptive weighted graph fusion incomplete multi-view subspace clustering. Sensors 20(20), 5755 (2020)","journal-title":"Sensors"},{"key":"1821_CR37","unstructured":"Zhao, H., Liu, H., Fu, Y.: Incomplete multi-modal visual data grouping. In: IJCAI, pp. 2392\u20132398 (2016)"},{"issue":"3","key":"1821_CR38","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s41019-021-00159-z","volume":"6","author":"G Du","year":"2021","unstructured":"Du, G., Zhou, L., Yang, Y., L\u00fc, K., Wang, L.: Deep multiple auto-encoder-based multi-view clustering. Data Sci. Eng. 6(3), 323\u2013338 (2021)","journal-title":"Data Sci. Eng."},{"key":"1821_CR39","doi-asserted-by":"crossref","unstructured":"Wen, J., Wu, Z., Zhang, Z., Fei, L., Zhang, B., Xu, Y.: Structural deep incomplete multi-view clustering network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3538\u20133542 (2021)","DOI":"10.1145\/3459637.3482192"},{"key":"1821_CR40","doi-asserted-by":"crossref","unstructured":"Lin, Y., Gou, Y., Liu, Z., Li, B., Lv, J., Peng, X.: Completer: incomplete multi-view clustering via contrastive prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11174\u201311183 (2021)","DOI":"10.1109\/CVPR46437.2021.01102"},{"key":"1821_CR41","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2020.12.094","volume":"433","author":"B Diallo","year":"2021","unstructured":"Diallo, B., Hu, J., Li, T., Khan, G.A., Liang, X., Zhao, Y.: Deep embedding clustering based on contractive autoencoder. Neurocomputing 433, 96\u2013107 (2021)","journal-title":"Neurocomputing"},{"key":"1821_CR42","doi-asserted-by":"crossref","unstructured":"Yu, Z., Yu, J., Xiang, C., Zhao, Z., Tian, Q., Tao, D.: Rethinking diversified and discriminative proposal generation for visual grounding (2018). arXiv preprint arXiv:1805.03508","DOI":"10.24963\/ijcai.2018\/155"},{"key":"1821_CR43","doi-asserted-by":"crossref","unstructured":"Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Interaction-and-aggregation network for person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9317\u20139326 (2019)","DOI":"10.1109\/CVPR.2019.00954"},{"key":"1821_CR44","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1821_CR45","unstructured":"Vaswani, A.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)"},{"key":"1821_CR46","doi-asserted-by":"crossref","unstructured":"Rush, A.: A neural attention model for abstractive sentence summarization (2015). arXiv Preprint, CoRR, arXiv:1509.00685","DOI":"10.18653\/v1\/D15-1044"},{"issue":"8","key":"1821_CR47","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.1109\/TNNLS.2021.3116209","volume":"34","author":"Z Xie","year":"2021","unstructured":"Xie, Z., Zhang, W., Sheng, B., Li, P., Chen, C.P.: Bagfn: broad attentive graph fusion network for high-order feature interactions. IEEE Trans. Neural Netw. Learn. Syst. 34(8), 4499\u20134513 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1821_CR48","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: Eapt: efficient attention pyramid transformer for image processing. IEEE Trans. Multimed. 25, 50\u201361 (2021)","journal-title":"IEEE Trans. Multimed."},{"issue":"12","key":"1821_CR49","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1109\/TMI.2021.3087857","volume":"40","author":"R Liu","year":"2021","unstructured":"Liu, R., Liu, M., Sheng, B., Li, H., Li, P., Song, H., Zhang, P., Jiang, L., Shen, D.: Nhbs-net: a feature fusion attention network for ultrasound neonatal hip bone segmentation. IEEE Trans. Med. Imaging 40(12), 3446\u20133458 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"1821_CR50","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.vrih.2022.08.016","volume":"5","author":"B Shen","year":"2023","unstructured":"Shen, B., Li, L., Hu, X., Guo, S., Huang, J., Liang, Z.: Point cloud upsampling generative adversarial network based on residual multi-scale off-set attention. Virtual Reality Intell. Hardware 5(1), 81\u201391 (2023)","journal-title":"Virtual Reality Intell. Hardware"},{"key":"1821_CR51","doi-asserted-by":"crossref","unstructured":"Qu, M., Tang, J., Shang, J., Ren, X., Zhang, M., Han, J.: An attention-based collaboration framework for multi-view network representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1767\u20131776 (2017)","DOI":"10.1145\/3132847.3133021"},{"key":"1821_CR52","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Shao, L.: Aware attentive multi-view inference for vehicle re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6489\u20136498 (2018)","DOI":"10.1109\/CVPR.2018.00679"},{"key":"1821_CR53","doi-asserted-by":"crossref","unstructured":"Huang, Z., Ren, Y., Pu, X., Huang, S., Xu, Z., He, L.: Self-supervised graph attention networks for deep weighted multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 7936\u20137943 (2023)","DOI":"10.1609\/aaai.v37i7.25960"},{"key":"1821_CR54","doi-asserted-by":"publisher","first-page":"109764","DOI":"10.1016\/j.patcog.2023.109764","volume":"143","author":"B Diallo","year":"2023","unstructured":"Diallo, B., Hu, J., Li, T., Khan, G.A., Liang, X., Wang, H.: Auto-attention mechanism for multi-view deep embedding clustering. Pattern Recognit. 143, 109764 (2023)","journal-title":"Pattern Recognit."},{"key":"1821_CR55","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1109\/TIP.2023.3243521","volume":"32","author":"J Xu","year":"2023","unstructured":"Xu, J., Li, C., Peng, L., Ren, Y., Shi, X., Shen, H.T., Zhu, X.: Adaptive feature projection with distribution alignment for deep incomplete multi-view clustering. IEEE Trans. Image Process. 32, 1354\u20131366 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"1821_CR56","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, p. 178. IEEE (2004)"},{"key":"1821_CR57","doi-asserted-by":"crossref","unstructured":"Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 2, pp. 524\u2013531. IEEE (2005)","DOI":"10.1109\/CVPR.2005.16"},{"issue":"4","key":"1821_CR58","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/LED.2018.2809661","volume":"39","author":"H Kim","year":"2018","unstructured":"Kim, H., Hwang, S., Park, J., Yun, S., Lee, J.-H., Park, B.-G.: Spiking neural network using synaptic transistors and neuron circuits for pattern recognition with noisy images. IEEE Electron Device Lett. 39(4), 630\u2013633 (2018)","journal-title":"IEEE Electron Device Lett."},{"key":"1821_CR59","doi-asserted-by":"crossref","unstructured":"Rai, N., Negi, S., Chaudhury, S., Deshmukh, O.: Partial multi-view clustering using graph regularized nmf. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2192\u20132197. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7899961"},{"key":"1821_CR60","doi-asserted-by":"crossref","unstructured":"Li, Z., Tang, C., Liu, X., Zheng, X., Zhang, W., Zhu, E.: Tensor-based multi-view block-diagonal structure diffusion for clustering incomplete multi-view data. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/ICME51207.2021.9428106"},{"issue":"4","key":"1821_CR61","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1109\/TETCI.2021.3077909","volume":"6","author":"X Fang","year":"2021","unstructured":"Fang, X., Hu, Y., Zhou, P., Wu, D.O.: Unbalanced incomplete multi-view clustering via the scheme of view evolution: weak views are meat; strong views do eat. IEEE Trans. Emerg. Topics Comput. Intell. 6(4), 913\u2013927 (2021)","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"key":"1821_CR62","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, X., Liu, L., Tu, W., Zhu, X., Liu, J., Zhou, S., Zhu, E.: Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9776\u20139785 (2022)","DOI":"10.1109\/CVPR52688.2022.00955"},{"key":"1821_CR63","doi-asserted-by":"crossref","unstructured":"Wei, S., Wang, J., Yu, G., Domeniconi, C., Zhang, X.: Deep incomplete multi-view multiple clusterings. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 651\u2013660. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00074"},{"key":"1821_CR64","doi-asserted-by":"crossref","unstructured":"Wen, J., Zhang, Z., Zhang, Z., Wu, Z., Fei, L., Xu, Y., Zhang, B.: Dimc-net: deep incomplete multi-view clustering network. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3753\u20133761 (2020)","DOI":"10.1145\/3394171.3413807"},{"key":"1821_CR65","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TIP.2020.3048626","volume":"30","author":"Q Wang","year":"2021","unstructured":"Wang, Q., Ding, Z., Tao, Z., Gao, Q., Fu, Y.: Generative partial multi-view clustering with adaptive fusion and cycle consistency. IEEE Trans. Image Process. 30, 1771\u20131783 (2021)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01821-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01821-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01821-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T15:02:30Z","timestamp":1756998150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01821-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,5]]},"references-count":65,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1821"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01821-6","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,5]]},"assertion":[{"value":"29 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"231"}}